Cursor vs. EarlyAI
AI developer tools have gained significant traction in recent years, revolutionizing how developers code, debug, and optimize their workflows. Tools like GitHub Copilot, Cursor IDE, and Early AI each bring unique strengths to the table. While they all belong to the same ecosystem, they serve different purposes and have distinct strengths. Cursor functions as an AI-enhanced code editor, while Early AI is specifically focused on generating unit tests and enhancing existing code quality.
In this article, we’ll compare Early AI with the Cursor IDE (Integrated Development Environment) in terms of their capabilities to improve code quality through unit test generation, bug detection, and other factors that impact development quality and speed.
Overview of Early AI and Cursor IDE
EarlyAI: An AI agent for test code generation focusing on improving existing code quality, capable of generating unit tests with high accuracy and extensive coverage at the click of a button. Acting as an “AI test engineer”, Early AI aims to enhance code quality by generating comprehensive tests, including mocking external calls, happy path scenarios, edge cases, method documentation, and more. With a strong focus on transforming test generation, Early AI saves developers time by automating substantial portion of the test code development workload
Cursor AI: A fork of the popular VSCode IDE, is an AI coding assistant designed as a general-purpose tool for code generation across various types of code (front-end, back-end, frameworks, testing, etc.) for many programming languages. While it provides some capabilities for test code suggestions, it functions as an "AI Assistant", offering versatility but lacking specialization. Cursor AI is beneficial for code completion and can suggest individual tests; however, its depth and focus on testing are limited compared to Early AI
Disclaimer: Please note that this comparison focuses solely on the test generation and improving existing code capabilities of Early AI vs. Cursor, other features are not evaluated in this article.
Setting the Stage: AI Assistants and AI Agents Explained
AI assistants, in general, are digital tools powered by artificial intelligence designed to help users perform tasks more efficiently by understanding natural language commands, anticipating needs, and providing intelligent suggestions. They leverage machine learning and natural language processing to understand context and deliver relevant responses or actions, often based on patterns learned from vast amounts of data.
For example, AI assistants can assist with writing by suggesting phrases, summarizing content, or rephrasing sentences, similar to how they help with code suggestions for developers. They can also support productivity by automating routine tasks, scheduling, and even answering questions or conducting research.
An AI agent is an autonomous software program that can perceive its environment, make decisions, and take actions to achieve specific goals or complete tasks. Unlike AI assistants, which often require ongoing user prompts or inputs, AI agents operate with a higher degree of independence. They continuously gather information from their surroundings, process it, and act based on pre-defined objectives, adapting their behavior over time.
Examples of AI agents include customer support chatbots that handle inquiries without human intervention, recommendation engines that suggest content based on user behavior, and robotic agents that navigate and interact within physical spaces. More advanced agents might even be capable of coordinating with other AI agents to achieve more complex goals collaboratively.
The key distinction of an AI agent is its autonomy and ability to operate proactively toward goals, often adapting based on feedback from its environment.
In the image below, you can see that, in the case of an AI agent, the user assigns a task to a system or product, which then interacts with large language models (LLMs) and other systems and data sources multiple times until it reaches a complete outcome.
Cursor vs. EarlyAI: Which Tool Will Help You Produce Higher Quality Code, Faster?
1. Output: coding assistant vs. coding Agent for test code generation
One of the biggest differences between Cursor and Early AI in terms of test code generation is that Cursor only suggests code, while Early AI addresses all code quality needs.
EarlyAI creates high-quality, functional unit tests that are directly saved in designated test files, either protecting against or exposing bugs. It generates a suite of test cases covering a broad range of scenarios, including both happy paths and edge cases, along with the necessary mocks and setup. Early AI’s output are running tests allowing developers to integrate these generated tests into their pull requests and, subsequently, into their CI/CD pipelines without extensive modification.
Cursor AI offers generic test cases, more often that are not compiling or fully running, giving developers a starting point for writing tests but leaving the integration and coverage largely up to the user. It may work well for small-scale needs such as verifying the functionality of specific methods or components but doesn’t reach the testing depth.
2. Test Quality: Green vs. Red unit tests
Early AI validates test results, creating green (passing) tests that capture the developer's intent and protect your code from bugs. It can also generate red (failing) tests. In many cases, these failing tests reveal bugs in your code, which aligns with Early AI’s ultimate goal: liberate software developers from bugs.
Cursor AI only generates code, it does not understand whether the code is running or failing, or whether the tests are high quality or not. The responsibility lies with the user to work with the output until it works.
3. Developer Efforts
EarlyAI focuses on minimizing or even fully eliminating manual intervention in testing. While not every test achieves 100% coverage or is entirely flawless, in most cases, EarlyAI generates comprehensive tests that require minimal additional effort from developers. More importantly, it empowers developers to identify and resolve bugs in their code effectively. This approach enables EarlyAI to generate over 1,000 reliable unit tests in under an hour, significantly reducing the testing workload.
Cursor AI on the other hand, centers on providing coding assistance for mundane, repetitive tasks or those that require developer oversight and interaction. It’s designed to help developers reach a specific point in their coding process more quickly but does not necessarily assist with the final steps. While Cursor can save a lot of time on initial coding tasks, it doesn't address the “last mile” needed to complete and polish the code.
4. Technologies and User Experience
Early AI as an AI Agent requires deep understanding of every technology, e.g. software language and test frameworks, it supports, hence currently (November 2024) only supporting TypeScript, JavaScript, Jest, and Mocha on VSCode. Soon we’ll be adding Python.
Cursor AI, is currently focused mostly on user experience integrating many different LLMs and letting the LLM do most of the test generation work, hence it supports the language and test framework the LLM supports.
5. IDE Compatibility and Integration
Cursor works as an AI-powered code editor with built-in capabilities like code completion, test suggestions, and other coding assistance. As a standalone code editor, Cursor AI provides an all-in-one experience but is limited to environments where the editor itself can be used. For teams looking for a complete code editing and assistance tool in a single platform, Cursor offers an integrated solution.
Early AI: Early AI is designed as a versatile test generation tool that can be layered on top of existing code editors like VS Code, Cursor, and soon IntelliJ. This compatibility allows developers to integrate Early AI’s test generation capabilities directly into their preferred development environments without needing to switch platforms. This flexibility makes Early AI a strong choice for teams using multiple IDEs or those who want to keep their primary code editor while adding robust, automated testing capabilities.
6. Impact: One test at a time vs. Scale
Cursor AI generates one test suggestion at a time. This approach is particularly useful for developers working on isolated tasks or smaller code changes where individual test suggestions are sufficient. However, when multiple tests are needed at once, developers may need to generate additional test cases manually, which can make it less suited to projects that require extensive, automated test coverage. For isolated tasks, Cursor saves significant time, but it may not support high-scale test generation
EarlyAI is designed to rapidly generate a suite of unit tests, making it effective for automating larger portions of the testing process. By generating dozens or hundreds of tests simultaneously—including standard cases, edge cases, and mock-based scenarios—EarlyAI helps teams achieve broad test coverage efficiently. This approach is useful for bringing low code coverage projects to high coverage instantly, continuous integration workflows and can support faster release cycles by reducing the time spent on test creation. Example of such results can be found on this benchmark blog.
So, Cursor AI or EarlyAI?
To summarize, Cursor and Early are designed for different purposes. Cursor is a general purpose code assist, supports all software languages and technologies, doing a nice job on everything but doesn’t complete the job. Early is designed for the task of test code generation and code quality, and frees up developers to do what they love best, build innovative applications (with the help of Cursor ☺).
Realistically, you don’t have to choose one over the other. Using EarlyAI alongside Cursor AI combines the benefits of an AI code editor with a comprehensive, automated testing agent, allowing developers to maximize code quality and speed of development. This combination allows Cursor to handle code assistance, while EarlyAI takes on test generation and creates a workflow that supports both speed and coverage. In some cases where Early’s results are not complete, Cursor can help finalize it and bring fantastic results.
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Although the article is written by a team member of Early, we have tried to be neutral and fair. A comparison might give you an idea of the differences as well as the similarities between the two tools. The best way to know if EarlyAI is the best tool for creating tests while providing code improvement and documentation suggestions is to try it by yourself.
You code, leave the Test to us!